Summary

The inferotemporal (IT) cortex in humans and other primates is topographically organized, containing multiple hierarchically organized areas selective for particular domains, such as faces and scenes. This organization is usually thought of in terms of domain-specific evolved visual mechanisms. Here, we develop an alternative, general and developmental domain account of computational cortical organization. The count is instantiated in Interactive Topographic Networks (ITNs), a class of computational models in which a hierarchy of model computational areas, subject to biologically plausible connectivity constraints, learns high-level visual representations optimized for multiple domains. We find that minimizing a wiring cost on the spatially organized feedforward and lateral connections, alongside realistic constraints on the sign of neural connectivity within the computational model, results in a hierarchical topographic organization. This organization replicates a number of key properties of the primate computational cortex, including the presence of domain-selective spatial clusters preferentially involved in the representation of faces, objects, and scenes; columnar responses through separate excitatory and inhibitory units; and a generic spatial organization in which the response correlation of pairs of units decreases with their distance. We therefore argue that topographic domain selectivity is an emergent property of a visual system optimized to maximize behavioral performance under generic connectivity-based constraints.